Overview

Dataset statistics

Number of variables55
Number of observations581012
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory243.8 MiB
Average record size in memory440.0 B

Variable types

Numeric11
Categorical44

Alerts

Aspect is highly overall correlated with Hillshade_3pmHigh correlation
Cover_Type is highly overall correlated with Wilderness_Area4High correlation
Elevation is highly overall correlated with Soil_Type10 and 2 other fieldsHigh correlation
Hillshade_3pm is highly overall correlated with Aspect and 2 other fieldsHigh correlation
Hillshade_9am is highly overall correlated with Hillshade_3pmHigh correlation
Hillshade_Noon is highly overall correlated with Hillshade_3pmHigh correlation
Horizontal_Distance_To_Hydrology is highly overall correlated with Vertical_Distance_To_HydrologyHigh correlation
Soil_Type10 is highly overall correlated with ElevationHigh correlation
Soil_Type29 is highly overall correlated with Wilderness_Area1High correlation
Soil_Type40 is highly overall correlated with ElevationHigh correlation
Vertical_Distance_To_Hydrology is highly overall correlated with Horizontal_Distance_To_HydrologyHigh correlation
Wilderness_Area1 is highly overall correlated with Soil_Type29 and 1 other fieldsHigh correlation
Wilderness_Area3 is highly overall correlated with Wilderness_Area1High correlation
Wilderness_Area4 is highly overall correlated with Cover_Type and 1 other fieldsHigh correlation
Wilderness_Area2 is highly imbalanced (70.8%)Imbalance
Wilderness_Area4 is highly imbalanced (65.8%)Imbalance
Soil_Type1 is highly imbalanced (95.3%)Imbalance
Soil_Type2 is highly imbalanced (90.0%)Imbalance
Soil_Type3 is highly imbalanced (93.1%)Imbalance
Soil_Type4 is highly imbalanced (85.1%)Imbalance
Soil_Type5 is highly imbalanced (97.3%)Imbalance
Soil_Type6 is highly imbalanced (91.1%)Imbalance
Soil_Type7 is highly imbalanced (99.7%)Imbalance
Soil_Type8 is highly imbalanced (99.6%)Imbalance
Soil_Type9 is highly imbalanced (97.9%)Imbalance
Soil_Type10 is highly imbalanced (68.8%)Imbalance
Soil_Type11 is highly imbalanced (85.1%)Imbalance
Soil_Type12 is highly imbalanced (70.7%)Imbalance
Soil_Type13 is highly imbalanced (80.6%)Imbalance
Soil_Type14 is highly imbalanced (98.8%)Imbalance
Soil_Type15 is highly imbalanced (> 99.9%)Imbalance
Soil_Type16 is highly imbalanced (95.5%)Imbalance
Soil_Type17 is highly imbalanced (94.8%)Imbalance
Soil_Type18 is highly imbalanced (96.8%)Imbalance
Soil_Type19 is highly imbalanced (94.0%)Imbalance
Soil_Type20 is highly imbalanced (88.2%)Imbalance
Soil_Type21 is highly imbalanced (98.4%)Imbalance
Soil_Type22 is highly imbalanced (68.3%)Imbalance
Soil_Type23 is highly imbalanced (53.3%)Imbalance
Soil_Type24 is highly imbalanced (77.3%)Imbalance
Soil_Type25 is highly imbalanced (99.0%)Imbalance
Soil_Type26 is highly imbalanced (95.9%)Imbalance
Soil_Type27 is highly imbalanced (98.0%)Imbalance
Soil_Type28 is highly imbalanced (98.3%)Imbalance
Soil_Type30 is highly imbalanced (70.5%)Imbalance
Soil_Type31 is highly imbalanced (73.9%)Imbalance
Soil_Type32 is highly imbalanced (56.2%)Imbalance
Soil_Type33 is highly imbalanced (60.6%)Imbalance
Soil_Type34 is highly imbalanced (97.2%)Imbalance
Soil_Type35 is highly imbalanced (96.8%)Imbalance
Soil_Type36 is highly imbalanced (99.7%)Imbalance
Soil_Type37 is highly imbalanced (99.4%)Imbalance
Soil_Type38 is highly imbalanced (82.2%)Imbalance
Soil_Type39 is highly imbalanced (83.8%)Imbalance
Soil_Type40 is highly imbalanced (88.7%)Imbalance
Horizontal_Distance_To_Hydrology has 24603 (4.2%) zerosZeros
Vertical_Distance_To_Hydrology has 38665 (6.7%) zerosZeros

Reproduction

Analysis started2025-12-11 14:17:14.324613
Analysis finished2025-12-11 14:19:32.602156
Duration2 minutes and 18.28 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Elevation
Real number (ℝ)

High correlation 

Distinct1978
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2959.3653
Minimum1859
Maximum3858
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-12-11T14:19:32.708358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1859
5-th percentile2406
Q12809
median2996
Q33163
95-th percentile3336
Maximum3858
Range1999
Interquartile range (IQR)354

Descriptive statistics

Standard deviation279.98473
Coefficient of variation (CV)0.094609724
Kurtosis0.74925078
Mean2959.3653
Median Absolute Deviation (MAD)175
Skewness-0.81759582
Sum1.7194268 × 109
Variance78391.451
MonotonicityNot monotonic
2025-12-11T14:19:32.847664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29681681
 
0.3%
29621674
 
0.3%
29911671
 
0.3%
29721662
 
0.3%
29751656
 
0.3%
29781656
 
0.3%
29881619
 
0.3%
29551590
 
0.3%
29521577
 
0.3%
29651571
 
0.3%
Other values (1968)564655
97.2%
ValueCountFrequency (%)
18591
 
< 0.1%
18601
 
< 0.1%
18611
 
< 0.1%
18631
 
< 0.1%
18661
 
< 0.1%
18671
 
< 0.1%
18681
 
< 0.1%
18713
< 0.1%
18724
< 0.1%
18731
 
< 0.1%
ValueCountFrequency (%)
38582
 
< 0.1%
38571
 
< 0.1%
38561
 
< 0.1%
38531
 
< 0.1%
38521
 
< 0.1%
38512
 
< 0.1%
38501
 
< 0.1%
38494
< 0.1%
38481
 
< 0.1%
38466
< 0.1%

Aspect
Real number (ℝ)

High correlation 

Distinct361
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.65681
Minimum0
Maximum360
Zeros4914
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-12-11T14:19:32.992572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q158
median127
Q3260
95-th percentile344
Maximum360
Range360
Interquartile range (IQR)202

Descriptive statistics

Standard deviation111.91372
Coefficient of variation (CV)0.71897736
Kurtosis-1.2202389
Mean155.65681
Median Absolute Deviation (MAD)85
Skewness0.40262832
Sum90438473
Variance12524.681
MonotonicityNot monotonic
2025-12-11T14:19:33.131268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
456308
 
1.1%
04914
 
0.8%
904677
 
0.8%
1353834
 
0.7%
633680
 
0.6%
3153574
 
0.6%
723407
 
0.6%
183403
 
0.6%
273392
 
0.6%
342836
 
0.5%
Other values (351)540987
93.1%
ValueCountFrequency (%)
04914
0.8%
11671
 
0.3%
21902
 
0.3%
31945
 
0.3%
42267
0.4%
52063
0.4%
62242
0.4%
72194
0.4%
82213
0.4%
92460
0.4%
ValueCountFrequency (%)
36051
 
< 0.1%
3591407
0.2%
3581749
0.3%
3571860
0.3%
3562025
0.3%
3551933
0.3%
3542025
0.3%
3531946
0.3%
3521985
0.3%
3512184
0.4%

Slope
Real number (ℝ)

Distinct67
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.103704
Minimum0
Maximum66
Zeros656
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-12-11T14:19:33.288246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q19
median13
Q318
95-th percentile28
Maximum66
Range66
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.4882418
Coefficient of variation (CV)0.53094152
Kurtosis0.58119911
Mean14.103704
Median Absolute Deviation (MAD)5
Skewness0.78927255
Sum8194421
Variance56.073765
MonotonicityNot monotonic
2025-12-11T14:19:33.430630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1133824
 
5.8%
1033812
 
5.8%
1233217
 
5.7%
1332419
 
5.6%
932049
 
5.5%
1430282
 
5.2%
830130
 
5.2%
1529127
 
5.0%
1626541
 
4.6%
726395
 
4.5%
Other values (57)273216
47.0%
ValueCountFrequency (%)
0656
 
0.1%
13680
 
0.6%
27726
 
1.3%
311620
 
2.0%
416344
2.8%
520810
3.6%
624504
4.2%
726395
4.5%
830130
5.2%
932049
5.5%
ValueCountFrequency (%)
661
 
< 0.1%
652
 
< 0.1%
641
 
< 0.1%
631
 
< 0.1%
622
 
< 0.1%
614
< 0.1%
602
 
< 0.1%
593
< 0.1%
581
 
< 0.1%
577
< 0.1%

Horizontal_Distance_To_Hydrology
Real number (ℝ)

High correlation  Zeros 

Distinct551
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean269.42822
Minimum0
Maximum1397
Zeros24603
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-12-11T14:19:33.563886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1108
median218
Q3384
95-th percentile684
Maximum1397
Range1397
Interquartile range (IQR)276

Descriptive statistics

Standard deviation212.54936
Coefficient of variation (CV)0.78889048
Kurtosis1.3661805
Mean269.42822
Median Absolute Deviation (MAD)133
Skewness1.1404374
Sum1.5654103 × 108
Variance45177.229
MonotonicityNot monotonic
2025-12-11T14:19:33.704846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3034139
 
5.9%
024603
 
4.2%
15020785
 
3.6%
6019189
 
3.3%
6715223
 
2.6%
4214647
 
2.5%
10814358
 
2.5%
8513741
 
2.4%
9011140
 
1.9%
12010673
 
1.8%
Other values (541)402514
69.3%
ValueCountFrequency (%)
024603
4.2%
3034139
5.9%
4214647
2.5%
6019189
3.3%
6715223
2.6%
8513741
2.4%
9011140
 
1.9%
959216
 
1.6%
10814358
2.5%
12010673
 
1.8%
ValueCountFrequency (%)
13971
< 0.1%
13902
< 0.1%
13832
< 0.1%
13821
< 0.1%
13761
< 0.1%
13711
< 0.1%
13701
< 0.1%
13691
< 0.1%
13682
< 0.1%
13612
< 0.1%

Vertical_Distance_To_Hydrology
Real number (ℝ)

High correlation  Zeros 

Distinct700
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.418855
Minimum-173
Maximum601
Zeros38665
Zeros (%)6.7%
Negative55143
Negative (%)9.5%
Memory size4.4 MiB
2025-12-11T14:19:33.848076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-173
5-th percentile-8
Q17
median30
Q369
95-th percentile165
Maximum601
Range774
Interquartile range (IQR)62

Descriptive statistics

Standard deviation58.295232
Coefficient of variation (CV)1.2558524
Kurtosis5.2502958
Mean46.418855
Median Absolute Deviation (MAD)27
Skewness1.7902497
Sum26969912
Variance3398.334
MonotonicityNot monotonic
2025-12-11T14:19:33.991348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
038665
 
6.7%
39298
 
1.6%
108863
 
1.5%
78741
 
1.5%
68590
 
1.5%
138474
 
1.5%
48397
 
1.4%
57614
 
1.3%
167429
 
1.3%
97331
 
1.3%
Other values (690)467610
80.5%
ValueCountFrequency (%)
-1731
 
< 0.1%
-1662
< 0.1%
-1641
 
< 0.1%
-1631
 
< 0.1%
-1611
 
< 0.1%
-1593
< 0.1%
-1581
 
< 0.1%
-1572
< 0.1%
-1562
< 0.1%
-1553
< 0.1%
ValueCountFrequency (%)
6011
 
< 0.1%
5991
 
< 0.1%
5982
< 0.1%
5973
< 0.1%
5952
< 0.1%
5921
 
< 0.1%
5911
 
< 0.1%
5902
< 0.1%
5893
< 0.1%
5883
< 0.1%
Distinct5785
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2350.1466
Minimum0
Maximum7117
Zeros124
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-12-11T14:19:34.136947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile379
Q11106
median1997
Q33328
95-th percentile5483
Maximum7117
Range7117
Interquartile range (IQR)2222

Descriptive statistics

Standard deviation1559.2549
Coefficient of variation (CV)0.66347132
Kurtosis-0.38371119
Mean2350.1466
Median Absolute Deviation (MAD)1040
Skewness0.71367882
Sum1.3654634 × 109
Variance2431275.7
MonotonicityNot monotonic
2025-12-11T14:19:34.303797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1501332
 
0.2%
6181065
 
0.2%
900918
 
0.2%
390914
 
0.2%
1020900
 
0.2%
990878
 
0.2%
960868
 
0.1%
997859
 
0.1%
750847
 
0.1%
1140840
 
0.1%
Other values (5775)571591
98.4%
ValueCountFrequency (%)
0124
 
< 0.1%
30313
0.1%
42171
 
< 0.1%
60312
0.1%
67298
0.1%
85384
0.1%
90380
0.1%
95374
0.1%
108660
0.1%
120633
0.1%
ValueCountFrequency (%)
71171
< 0.1%
71161
< 0.1%
71121
< 0.1%
70971
< 0.1%
70921
< 0.1%
70872
< 0.1%
70821
< 0.1%
70791
< 0.1%
70782
< 0.1%
70691
< 0.1%

Hillshade_9am
Real number (ℝ)

High correlation 

Distinct207
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.14605
Minimum0
Maximum254
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-12-11T14:19:34.448376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile160
Q1198
median218
Q3231
95-th percentile246
Maximum254
Range254
Interquartile range (IQR)33

Descriptive statistics

Standard deviation26.769889
Coefficient of variation (CV)0.12618613
Kurtosis1.8755177
Mean212.14605
Median Absolute Deviation (MAD)16
Skewness-1.1811467
Sum1.232594 × 108
Variance716.62695
MonotonicityNot monotonic
2025-12-11T14:19:34.586713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22611657
 
2.0%
22811374
 
2.0%
23011355
 
2.0%
22411210
 
1.9%
22310887
 
1.9%
22210809
 
1.9%
23310645
 
1.8%
22710513
 
1.8%
22510307
 
1.8%
22110264
 
1.8%
Other values (197)471991
81.2%
ValueCountFrequency (%)
013
< 0.1%
361
 
< 0.1%
462
 
< 0.1%
501
 
< 0.1%
522
 
< 0.1%
531
 
< 0.1%
544
 
< 0.1%
551
 
< 0.1%
566
< 0.1%
572
 
< 0.1%
ValueCountFrequency (%)
2541898
 
0.3%
2532236
0.4%
2522563
0.4%
2512968
0.5%
2503341
0.6%
2493793
0.7%
2483955
0.7%
2474443
0.8%
2465008
0.9%
2455530
1.0%

Hillshade_Noon
Real number (ℝ)

High correlation 

Distinct185
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223.31872
Minimum0
Maximum254
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-12-11T14:19:34.722754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile186
Q1213
median226
Q3237
95-th percentile250
Maximum254
Range254
Interquartile range (IQR)24

Descriptive statistics

Standard deviation19.768697
Coefficient of variation (CV)0.088522348
Kurtosis2.0662108
Mean223.31872
Median Absolute Deviation (MAD)12
Skewness-1.0630563
Sum1.2975085 × 108
Variance390.80139
MonotonicityNot monotonic
2025-12-11T14:19:34.861319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22813696
 
2.4%
23113666
 
2.4%
23313297
 
2.3%
22913271
 
2.3%
23013258
 
2.3%
23413047
 
2.2%
22713020
 
2.2%
22312989
 
2.2%
22612953
 
2.2%
22512928
 
2.2%
Other values (175)448887
77.3%
ValueCountFrequency (%)
05
< 0.1%
301
 
< 0.1%
401
 
< 0.1%
421
 
< 0.1%
451
 
< 0.1%
532
 
< 0.1%
631
 
< 0.1%
641
 
< 0.1%
681
 
< 0.1%
711
 
< 0.1%
ValueCountFrequency (%)
2545902
1.0%
2536300
1.1%
2527171
1.2%
2517471
1.3%
2508028
1.4%
2497714
1.3%
2488133
1.4%
2478874
1.5%
2468665
1.5%
2458538
1.5%

Hillshade_3pm
Real number (ℝ)

High correlation 

Distinct255
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.52826
Minimum0
Maximum254
Zeros1338
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-12-11T14:19:35.004127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile78
Q1119
median143
Q3168
95-th percentile204
Maximum254
Range254
Interquartile range (IQR)49

Descriptive statistics

Standard deviation38.274529
Coefficient of variation (CV)0.26853993
Kurtosis0.39844001
Mean142.52826
Median Absolute Deviation (MAD)25
Skewness-0.2770532
Sum82810631
Variance1464.9396
MonotonicityNot monotonic
2025-12-11T14:19:35.157899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1437333
 
1.3%
1457217
 
1.2%
1387065
 
1.2%
1466915
 
1.2%
1426902
 
1.2%
1366871
 
1.2%
1396858
 
1.2%
1356781
 
1.2%
1496723
 
1.2%
1326673
 
1.1%
Other values (245)511674
88.1%
ValueCountFrequency (%)
01338
0.2%
115
 
< 0.1%
215
 
< 0.1%
315
 
< 0.1%
420
 
< 0.1%
518
 
< 0.1%
626
 
< 0.1%
730
 
< 0.1%
821
 
< 0.1%
933
 
< 0.1%
ValueCountFrequency (%)
2544
 
< 0.1%
2538
 
< 0.1%
25216
 
< 0.1%
25111
 
< 0.1%
25017
 
< 0.1%
24937
< 0.1%
24844
< 0.1%
24761
< 0.1%
24672
< 0.1%
24585
< 0.1%
Distinct5827
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1980.2912
Minimum0
Maximum7173
Zeros51
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-12-11T14:19:35.320778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile418
Q11024
median1710
Q32550
95-th percentile4944
Maximum7173
Range7173
Interquartile range (IQR)1526

Descriptive statistics

Standard deviation1324.1952
Coefficient of variation (CV)0.66868711
Kurtosis1.6458068
Mean1980.2912
Median Absolute Deviation (MAD)750
Skewness1.2886441
Sum1.150573 × 109
Variance1753493
MonotonicityNot monotonic
2025-12-11T14:19:35.471709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6181412
 
0.2%
5411099
 
0.2%
6071054
 
0.2%
9421023
 
0.2%
9971004
 
0.2%
700958
 
0.2%
900937
 
0.2%
726923
 
0.2%
752910
 
0.2%
960908
 
0.2%
Other values (5817)570784
98.2%
ValueCountFrequency (%)
051
 
< 0.1%
30206
< 0.1%
42207
< 0.1%
60206
< 0.1%
67416
0.1%
85207
< 0.1%
90204
< 0.1%
95412
0.1%
108412
0.1%
120204
< 0.1%
ValueCountFrequency (%)
71731
< 0.1%
71721
< 0.1%
71681
< 0.1%
71501
< 0.1%
71451
< 0.1%
71421
< 0.1%
71412
< 0.1%
71401
< 0.1%
71311
< 0.1%
71261
< 0.1%

Wilderness_Area1
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
320216 
1
260796 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0320216
55.1%
1260796
44.9%

Length

2025-12-11T14:19:35.608541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:35.696441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0320216
55.1%
1260796
44.9%

Most occurring characters

ValueCountFrequency (%)
0320216
55.1%
1260796
44.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0320216
55.1%
1260796
44.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0320216
55.1%
1260796
44.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0320216
55.1%
1260796
44.9%

Wilderness_Area2
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
551128 
1
 
29884

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0551128
94.9%
129884
 
5.1%

Length

2025-12-11T14:19:35.790493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:35.867028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0551128
94.9%
129884
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0551128
94.9%
129884
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0551128
94.9%
129884
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0551128
94.9%
129884
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0551128
94.9%
129884
 
5.1%

Wilderness_Area3
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
327648 
1
253364 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0327648
56.4%
1253364
43.6%

Length

2025-12-11T14:19:35.956557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:36.034227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0327648
56.4%
1253364
43.6%

Most occurring characters

ValueCountFrequency (%)
0327648
56.4%
1253364
43.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0327648
56.4%
1253364
43.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0327648
56.4%
1253364
43.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0327648
56.4%
1253364
43.6%

Wilderness_Area4
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
544044 
1
 
36968

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0544044
93.6%
136968
 
6.4%

Length

2025-12-11T14:19:36.129282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:36.209735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0544044
93.6%
136968
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0544044
93.6%
136968
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0544044
93.6%
136968
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0544044
93.6%
136968
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0544044
93.6%
136968
 
6.4%

Soil_Type1
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
577981 
1
 
3031

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0577981
99.5%
13031
 
0.5%

Length

2025-12-11T14:19:36.305683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:36.395289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0577981
99.5%
13031
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0577981
99.5%
13031
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0577981
99.5%
13031
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0577981
99.5%
13031
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0577981
99.5%
13031
 
0.5%

Soil_Type2
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
573487 
1
 
7525

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0573487
98.7%
17525
 
1.3%

Length

2025-12-11T14:19:36.483371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:36.559136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0573487
98.7%
17525
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0573487
98.7%
17525
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0573487
98.7%
17525
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0573487
98.7%
17525
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0573487
98.7%
17525
 
1.3%

Soil_Type3
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
576189 
1
 
4823

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0576189
99.2%
14823
 
0.8%

Length

2025-12-11T14:19:36.647486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:36.719079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0576189
99.2%
14823
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0576189
99.2%
14823
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0576189
99.2%
14823
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0576189
99.2%
14823
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0576189
99.2%
14823
 
0.8%

Soil_Type4
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
568616 
1
 
12396

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0568616
97.9%
112396
 
2.1%

Length

2025-12-11T14:19:37.200938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:37.281547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0568616
97.9%
112396
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0568616
97.9%
112396
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0568616
97.9%
112396
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0568616
97.9%
112396
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0568616
97.9%
112396
 
2.1%

Soil_Type5
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
579415 
1
 
1597

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0579415
99.7%
11597
 
0.3%

Length

2025-12-11T14:19:37.374261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:37.465907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0579415
99.7%
11597
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0579415
99.7%
11597
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0579415
99.7%
11597
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0579415
99.7%
11597
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0579415
99.7%
11597
 
0.3%

Soil_Type6
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
574437 
1
 
6575

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0574437
98.9%
16575
 
1.1%

Length

2025-12-11T14:19:37.579168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:37.703898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0574437
98.9%
16575
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0574437
98.9%
16575
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0574437
98.9%
16575
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0574437
98.9%
16575
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0574437
98.9%
16575
 
1.1%

Soil_Type7
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
580907 
1
 
105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0580907
> 99.9%
1105
 
< 0.1%

Length

2025-12-11T14:19:37.833742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:37.943952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0580907
> 99.9%
1105
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0580907
> 99.9%
1105
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0580907
> 99.9%
1105
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0580907
> 99.9%
1105
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0580907
> 99.9%
1105
 
< 0.1%

Soil_Type8
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
580833 
1
 
179

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0580833
> 99.9%
1179
 
< 0.1%

Length

2025-12-11T14:19:38.072938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:38.180557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0580833
> 99.9%
1179
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0580833
> 99.9%
1179
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0580833
> 99.9%
1179
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0580833
> 99.9%
1179
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0580833
> 99.9%
1179
 
< 0.1%

Soil_Type9
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
579865 
1
 
1147

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0579865
99.8%
11147
 
0.2%

Length

2025-12-11T14:19:38.315960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:38.426042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0579865
99.8%
11147
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0579865
99.8%
11147
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0579865
99.8%
11147
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0579865
99.8%
11147
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0579865
99.8%
11147
 
0.2%

Soil_Type10
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
548378 
1
 
32634

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0548378
94.4%
132634
 
5.6%

Length

2025-12-11T14:19:38.561307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:38.670775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0548378
94.4%
132634
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0548378
94.4%
132634
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0548378
94.4%
132634
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0548378
94.4%
132634
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0548378
94.4%
132634
 
5.6%

Soil_Type11
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
568602 
1
 
12410

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0568602
97.9%
112410
 
2.1%

Length

2025-12-11T14:19:38.795947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:38.898096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0568602
97.9%
112410
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0568602
97.9%
112410
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0568602
97.9%
112410
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0568602
97.9%
112410
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0568602
97.9%
112410
 
2.1%

Soil_Type12
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
551041 
1
 
29971

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0551041
94.8%
129971
 
5.2%

Length

2025-12-11T14:19:39.019998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:39.123691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0551041
94.8%
129971
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0551041
94.8%
129971
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0551041
94.8%
129971
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0551041
94.8%
129971
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0551041
94.8%
129971
 
5.2%

Soil_Type13
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
563581 
1
 
17431

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0563581
97.0%
117431
 
3.0%

Length

2025-12-11T14:19:39.258864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:39.378401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0563581
97.0%
117431
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0563581
97.0%
117431
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0563581
97.0%
117431
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0563581
97.0%
117431
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0563581
97.0%
117431
 
3.0%

Soil_Type14
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
580413 
1
 
599

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0580413
99.9%
1599
 
0.1%

Length

2025-12-11T14:19:39.514188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:39.632629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0580413
99.9%
1599
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0580413
99.9%
1599
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0580413
99.9%
1599
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0580413
99.9%
1599
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0580413
99.9%
1599
 
0.1%

Soil_Type15
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
581009 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0581009
> 99.9%
13
 
< 0.1%

Length

2025-12-11T14:19:39.781804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:39.898830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0581009
> 99.9%
13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0581009
> 99.9%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0581009
> 99.9%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0581009
> 99.9%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0581009
> 99.9%
13
 
< 0.1%

Soil_Type16
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
578167 
1
 
2845

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0578167
99.5%
12845
 
0.5%

Length

2025-12-11T14:19:40.038647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:40.150340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0578167
99.5%
12845
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0578167
99.5%
12845
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0578167
99.5%
12845
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0578167
99.5%
12845
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0578167
99.5%
12845
 
0.5%

Soil_Type17
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
577590 
1
 
3422

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0577590
99.4%
13422
 
0.6%

Length

2025-12-11T14:19:40.243779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:40.319479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0577590
99.4%
13422
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0577590
99.4%
13422
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0577590
99.4%
13422
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0577590
99.4%
13422
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0577590
99.4%
13422
 
0.6%

Soil_Type18
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
579113 
1
 
1899

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0579113
99.7%
11899
 
0.3%

Length

2025-12-11T14:19:40.405433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:40.481383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0579113
99.7%
11899
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0579113
99.7%
11899
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0579113
99.7%
11899
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0579113
99.7%
11899
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0579113
99.7%
11899
 
0.3%

Soil_Type19
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
576991 
1
 
4021

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0576991
99.3%
14021
 
0.7%

Length

2025-12-11T14:19:40.572411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:40.663076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0576991
99.3%
14021
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0576991
99.3%
14021
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0576991
99.3%
14021
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0576991
99.3%
14021
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0576991
99.3%
14021
 
0.7%

Soil_Type20
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
571753 
1
 
9259

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0571753
98.4%
19259
 
1.6%

Length

2025-12-11T14:19:40.752441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:40.824345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0571753
98.4%
19259
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0571753
98.4%
19259
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0571753
98.4%
19259
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0571753
98.4%
19259
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0571753
98.4%
19259
 
1.6%

Soil_Type21
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
580174 
1
 
838

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0580174
99.9%
1838
 
0.1%

Length

2025-12-11T14:19:40.912173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:40.986785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0580174
99.9%
1838
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0580174
99.9%
1838
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0580174
99.9%
1838
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0580174
99.9%
1838
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0580174
99.9%
1838
 
0.1%

Soil_Type22
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
547639 
1
 
33373

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0547639
94.3%
133373
 
5.7%

Length

2025-12-11T14:19:41.071285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:41.151770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0547639
94.3%
133373
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0547639
94.3%
133373
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0547639
94.3%
133373
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0547639
94.3%
133373
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0547639
94.3%
133373
 
5.7%

Soil_Type23
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
523260 
1
57752 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0523260
90.1%
157752
 
9.9%

Length

2025-12-11T14:19:41.249902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:41.329126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0523260
90.1%
157752
 
9.9%

Most occurring characters

ValueCountFrequency (%)
0523260
90.1%
157752
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0523260
90.1%
157752
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0523260
90.1%
157752
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0523260
90.1%
157752
 
9.9%

Soil_Type24
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
559734 
1
 
21278

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0559734
96.3%
121278
 
3.7%

Length

2025-12-11T14:19:41.422761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:41.499713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0559734
96.3%
121278
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0559734
96.3%
121278
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0559734
96.3%
121278
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0559734
96.3%
121278
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0559734
96.3%
121278
 
3.7%

Soil_Type25
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
580538 
1
 
474

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0580538
99.9%
1474
 
0.1%

Length

2025-12-11T14:19:41.587230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:41.675903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0580538
99.9%
1474
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0580538
99.9%
1474
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0580538
99.9%
1474
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0580538
99.9%
1474
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0580538
99.9%
1474
 
0.1%

Soil_Type26
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
578423 
1
 
2589

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0578423
99.6%
12589
 
0.4%

Length

2025-12-11T14:19:41.766649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:41.840309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0578423
99.6%
12589
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0578423
99.6%
12589
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0578423
99.6%
12589
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0578423
99.6%
12589
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0578423
99.6%
12589
 
0.4%

Soil_Type27
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
579926 
1
 
1086

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0579926
99.8%
11086
 
0.2%

Length

2025-12-11T14:19:41.937436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:42.013553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0579926
99.8%
11086
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0579926
99.8%
11086
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0579926
99.8%
11086
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0579926
99.8%
11086
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0579926
99.8%
11086
 
0.2%

Soil_Type28
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
580066 
1
 
946

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0580066
99.8%
1946
 
0.2%

Length

2025-12-11T14:19:42.101057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:42.180315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0580066
99.8%
1946
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0580066
99.8%
1946
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0580066
99.8%
1946
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0580066
99.8%
1946
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0580066
99.8%
1946
 
0.2%

Soil_Type29
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
465765 
1
115247 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0465765
80.2%
1115247
 
19.8%

Length

2025-12-11T14:19:42.269757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:42.346798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0465765
80.2%
1115247
 
19.8%

Most occurring characters

ValueCountFrequency (%)
0465765
80.2%
1115247
 
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0465765
80.2%
1115247
 
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0465765
80.2%
1115247
 
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0465765
80.2%
1115247
 
19.8%

Soil_Type30
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
550842 
1
 
30170

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0550842
94.8%
130170
 
5.2%

Length

2025-12-11T14:19:42.442741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:42.520284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0550842
94.8%
130170
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0550842
94.8%
130170
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0550842
94.8%
130170
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0550842
94.8%
130170
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0550842
94.8%
130170
 
5.2%

Soil_Type31
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
555346 
1
 
25666

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0555346
95.6%
125666
 
4.4%

Length

2025-12-11T14:19:42.612130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:42.697937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0555346
95.6%
125666
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0555346
95.6%
125666
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0555346
95.6%
125666
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0555346
95.6%
125666
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0555346
95.6%
125666
 
4.4%

Soil_Type32
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
528493 
1
 
52519

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0528493
91.0%
152519
 
9.0%

Length

2025-12-11T14:19:42.784323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:42.859224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0528493
91.0%
152519
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0528493
91.0%
152519
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0528493
91.0%
152519
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0528493
91.0%
152519
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0528493
91.0%
152519
 
9.0%

Soil_Type33
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
535858 
1
 
45154

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0535858
92.2%
145154
 
7.8%

Length

2025-12-11T14:19:42.948050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:43.020200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0535858
92.2%
145154
 
7.8%

Most occurring characters

ValueCountFrequency (%)
0535858
92.2%
145154
 
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0535858
92.2%
145154
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0535858
92.2%
145154
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0535858
92.2%
145154
 
7.8%

Soil_Type34
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
579401 
1
 
1611

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0579401
99.7%
11611
 
0.3%

Length

2025-12-11T14:19:43.107104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:43.186332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0579401
99.7%
11611
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0579401
99.7%
11611
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0579401
99.7%
11611
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0579401
99.7%
11611
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0579401
99.7%
11611
 
0.3%

Soil_Type35
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
579121 
1
 
1891

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0579121
99.7%
11891
 
0.3%

Length

2025-12-11T14:19:43.274427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:43.349578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0579121
99.7%
11891
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0579121
99.7%
11891
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0579121
99.7%
11891
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0579121
99.7%
11891
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0579121
99.7%
11891
 
0.3%

Soil_Type36
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
580893 
1
 
119

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0580893
> 99.9%
1119
 
< 0.1%

Length

2025-12-11T14:19:43.438515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:43.509863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0580893
> 99.9%
1119
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0580893
> 99.9%
1119
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0580893
> 99.9%
1119
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0580893
> 99.9%
1119
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0580893
> 99.9%
1119
 
< 0.1%

Soil_Type37
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
580714 
1
 
298

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0580714
99.9%
1298
 
0.1%

Length

2025-12-11T14:19:43.592774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:43.666712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0580714
99.9%
1298
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0580714
99.9%
1298
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0580714
99.9%
1298
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0580714
99.9%
1298
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0580714
99.9%
1298
 
0.1%

Soil_Type38
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
565439 
1
 
15573

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0565439
97.3%
115573
 
2.7%

Length

2025-12-11T14:19:43.766795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:43.840336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0565439
97.3%
115573
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0565439
97.3%
115573
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0565439
97.3%
115573
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0565439
97.3%
115573
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0565439
97.3%
115573
 
2.7%

Soil_Type39
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
567206 
1
 
13806

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0567206
97.6%
113806
 
2.4%

Length

2025-12-11T14:19:43.927688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:44.001553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0567206
97.6%
113806
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0567206
97.6%
113806
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0567206
97.6%
113806
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0567206
97.6%
113806
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0567206
97.6%
113806
 
2.4%

Soil_Type40
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
572262 
1
 
8750

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0572262
98.5%
18750
 
1.5%

Length

2025-12-11T14:19:44.092501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-11T14:19:44.170347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0572262
98.5%
18750
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0572262
98.5%
18750
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0572262
98.5%
18750
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0572262
98.5%
18750
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)581012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0572262
98.5%
18750
 
1.5%

Cover_Type
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0514705
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-12-11T14:19:44.232241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3965043
Coefficient of variation (CV)0.6807333
Kurtosis4.9489652
Mean2.0514705
Median Absolute Deviation (MAD)1
Skewness2.2765737
Sum1191929
Variance1.9502243
MonotonicityNot monotonic
2025-12-11T14:19:44.319113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2283301
48.8%
1211840
36.5%
335754
 
6.2%
720510
 
3.5%
617367
 
3.0%
59493
 
1.6%
42747
 
0.5%
ValueCountFrequency (%)
1211840
36.5%
2283301
48.8%
335754
 
6.2%
42747
 
0.5%
59493
 
1.6%
617367
 
3.0%
720510
 
3.5%
ValueCountFrequency (%)
720510
 
3.5%
617367
 
3.0%
59493
 
1.6%
42747
 
0.5%
335754
 
6.2%
2283301
48.8%
1211840
36.5%

Interactions

2025-12-11T14:19:23.558143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:18:59.345880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:02.388870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:04.785532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:06.930748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:09.936861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:12.265345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:15.165149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:17.192955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:19.450318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:21.479538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:23.738242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:18:59.657519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:02.591557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:04.998215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:07.145831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:10.231042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:12.558816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:15.355349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:17.642397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:19.634610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:21.668521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:23.929372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:18:59.921104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:02.801054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:05.198467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:07.353924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:10.490486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:12.833029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:15.542637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:17.821203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:19.832304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:21.873488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:24.106149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:00.191686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:02.984849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:05.382285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:07.541825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:10.683971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:13.093268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:15.722327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:18.000031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:20.015440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:22.055806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:24.303324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:00.461557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:03.182712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:05.578563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:07.745570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:10.878673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:13.375760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:15.909950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:18.186573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:20.217567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:22.253348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:24.486194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:00.751556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:03.560680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:05.775742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:08.064248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:11.073236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:13.639477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:16.097996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:18.375160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:20.407231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:22.446584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:24.674614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:01.013965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:03.756822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:05.983778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:08.349212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:11.288435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:13.903958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:16.282913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:18.558887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:20.592500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:22.633600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:24.852622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:01.277411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:03.967851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:06.174377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:08.615813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:11.479448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:14.170253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:16.457217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:18.741631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:20.775716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:22.821992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:25.071303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:01.548292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:04.169212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:06.374807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:08.875424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:11.672224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:14.430494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:16.642169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:18.912140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:20.950540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:23.001056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:25.332582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:01.856055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:04.366723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:06.554164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:09.136484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:11.861506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:14.719511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:16.817472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:19.080584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:21.121715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:23.184771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:25.975151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:02.176619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:04.573700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:06.745521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:09.399318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:12.055836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:14.979442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:16.998889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:19.265038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:21.310271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T14:19:23.373274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-11T14:19:44.974725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AspectCover_TypeElevationHillshade_3pmHillshade_9amHillshade_NoonHorizontal_Distance_To_Fire_PointsHorizontal_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysSlopeSoil_Type1Soil_Type10Soil_Type11Soil_Type12Soil_Type13Soil_Type14Soil_Type15Soil_Type16Soil_Type17Soil_Type18Soil_Type19Soil_Type2Soil_Type20Soil_Type21Soil_Type22Soil_Type23Soil_Type24Soil_Type25Soil_Type26Soil_Type27Soil_Type28Soil_Type29Soil_Type3Soil_Type30Soil_Type31Soil_Type32Soil_Type33Soil_Type34Soil_Type35Soil_Type36Soil_Type37Soil_Type38Soil_Type39Soil_Type4Soil_Type40Soil_Type5Soil_Type6Soil_Type7Soil_Type8Soil_Type9Vertical_Distance_To_HydrologyWilderness_Area1Wilderness_Area2Wilderness_Area3Wilderness_Area4
Aspect1.0000.0250.0440.641-0.4290.421-0.1130.0050.0190.0720.0480.1590.0780.0940.1730.0130.0040.0190.0180.0460.0110.0640.0450.0480.0420.0280.1590.0590.0740.0670.0690.1110.1060.1320.0920.0950.0660.0610.0300.0370.0270.0320.0460.1770.0340.0260.0260.0070.0040.0310.0730.2090.0710.1910.130
Cover_Type0.0251.000-0.491-0.0360.013-0.034-0.137-0.028-0.2220.1510.2240.4710.1090.1980.1380.1570.0130.0370.1930.0590.0480.3100.0450.0470.2120.1870.0840.0150.0530.0200.0370.1970.3260.1480.0800.1070.0940.0430.1490.0380.1180.3410.3310.3420.2660.1630.2820.0130.0100.0330.0960.3110.1500.1120.736
Elevation0.044-0.4911.0000.0730.0150.1500.1550.2870.342-0.1600.3640.5140.2180.2780.1490.1290.0160.0700.1320.1900.0680.2540.0880.0240.2240.1930.0880.0360.0710.0410.0620.2380.2270.1230.0930.1900.0980.0330.1360.0400.0670.3860.3130.2390.6220.2880.3110.0200.0260.1530.0870.2790.2980.1990.881
Hillshade_3pm0.641-0.0360.0731.000-0.8230.574-0.0830.0380.102-0.1730.1480.1330.0860.1390.2030.0090.0000.0360.0380.0500.0580.0190.0680.0460.0460.1410.0280.0360.0330.0550.1730.0920.1260.1320.0570.1260.1270.0270.0300.0130.0240.0570.0870.0550.0400.0590.0160.0110.0160.0270.0380.1870.0550.1150.186
Hillshade_9am-0.4290.0130.015-0.8231.000-0.1010.124-0.042-0.010-0.1310.0480.2560.0570.1190.1240.0120.0000.0250.0250.0350.0540.0370.0550.0310.0410.1230.1220.0400.0380.0470.1520.0840.0560.1380.0830.0980.1000.0190.0290.0090.0120.0390.0700.0480.0170.0710.0240.0110.0170.026-0.1290.2110.0280.1320.257
Hillshade_Noon0.421-0.0340.1500.574-0.1011.0000.0170.0280.174-0.4340.0900.2500.0740.0810.0840.0000.0010.0180.0300.0220.0490.0420.0360.0320.0390.1310.1410.0100.0330.0240.0080.0800.0360.0430.0510.1210.1290.0380.0150.0160.0240.0460.0720.0780.0410.0790.0170.0060.0140.013-0.0960.0990.0490.0670.220
Horizontal_Distance_To_Fire_Points-0.113-0.1370.155-0.0830.1240.0171.0000.0740.322-0.1700.1170.2090.0780.2960.1110.0510.0040.1030.0400.1570.0250.0970.1220.0300.0540.0790.0700.0740.0730.0440.0380.2160.1000.0740.0780.1060.0910.0460.0500.0210.0350.0930.0440.0870.0640.0660.1080.0820.0420.052-0.0430.3810.1130.3100.317
Horizontal_Distance_To_Hydrology0.005-0.0280.2870.038-0.0420.0280.0741.0000.0470.0190.0350.0780.0230.0760.0190.0460.0000.0760.0920.0200.0560.0470.1020.0480.0570.1670.0380.0270.0190.1110.0330.0720.0430.0540.0750.1390.0990.1050.0360.0670.0170.0700.0430.0500.1660.0160.0410.0190.0100.0250.6190.1090.0680.1360.103
Horizontal_Distance_To_Roadways0.019-0.2220.3420.102-0.0100.1740.3220.0471.000-0.2050.1320.2040.1070.0990.1200.0450.0040.0550.0590.0730.0920.0930.0890.0370.1330.0500.0680.0730.0780.0500.0710.3310.1010.1210.1450.1580.1360.0460.0510.0330.0470.0910.1110.0950.0690.0990.1410.0540.0470.062-0.0380.4830.2260.3990.357
Slope0.0720.151-0.160-0.173-0.131-0.434-0.1700.019-0.2051.0000.1220.2470.0710.1750.2010.0010.0000.0370.0440.0480.1060.0210.0790.0260.0570.2120.1140.0360.0450.0510.0900.0950.1440.0810.0840.1400.2310.0130.0240.0150.0090.0740.0960.1380.0310.0850.0120.0200.0350.0340.3010.2310.0410.1480.276
Soil_Type10.0480.2240.3640.1480.0480.0900.1170.0350.1320.1221.0000.0180.0110.0170.0130.0010.0000.0050.0050.0040.0060.0080.0090.0020.0180.0240.0140.0010.0040.0030.0020.0360.0060.0170.0150.0230.0210.0030.0040.0000.0000.0120.0110.0110.0090.0030.0080.0000.0000.0030.0230.0650.0170.0640.278
Soil_Type100.1590.4710.5140.1330.2560.2500.2090.0780.2040.2470.0181.0000.0360.0570.0430.0080.0000.0170.0190.0140.0200.0280.0310.0090.0600.0810.0480.0070.0160.0100.0100.1210.0220.0570.0520.0770.0710.0130.0140.0030.0050.0400.0380.0360.0300.0130.0260.0030.0040.0110.0670.2200.0570.0070.485
Soil_Type110.0780.1090.2180.0860.0570.0740.0780.0230.1070.0710.0110.0361.0000.0340.0260.0040.0000.0100.0110.0080.0120.0170.0190.0050.0360.0490.0290.0040.0100.0060.0060.0730.0130.0350.0320.0470.0430.0080.0080.0010.0030.0240.0230.0220.0180.0080.0160.0010.0020.0060.0300.1330.0340.1540.009
Soil_Type120.0940.1980.2780.1390.1190.0810.2960.0760.0990.1750.0170.0570.0341.0000.0410.0070.0000.0160.0180.0130.0190.0270.0300.0090.0580.0770.0450.0060.0150.0100.0090.1160.0210.0550.0500.0730.0680.0120.0130.0030.0050.0390.0360.0340.0290.0120.0250.0030.0040.0100.0710.2580.0540.2050.061
Soil_Type130.1730.1380.1490.2030.1240.0840.1110.0190.1200.2010.0130.0430.0260.0411.0000.0050.0000.0120.0130.0100.0150.0200.0220.0060.0430.0580.0340.0050.0120.0070.0070.0870.0160.0410.0380.0550.0510.0090.0100.0020.0040.0290.0270.0260.0220.0090.0190.0010.0020.0080.0870.1590.0290.1950.046
Soil_Type140.0130.1570.1290.0090.0120.0000.0510.0460.0450.0010.0010.0080.0040.0070.0051.0000.0000.0010.0020.0000.0020.0030.0040.0000.0080.0110.0060.0000.0010.0000.0000.0160.0020.0070.0070.0100.0090.0000.0000.0000.0000.0050.0050.0040.0040.0000.0030.0000.0000.0000.0220.0290.0070.0020.070
Soil_Type150.0040.0130.0160.0000.0000.0010.0040.0000.0040.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.007
Soil_Type160.0190.0370.0700.0360.0250.0180.1030.0760.0550.0370.0050.0170.0100.0160.0120.0010.0001.0000.0050.0040.0060.0080.0090.0020.0170.0230.0140.0010.0040.0020.0020.0350.0060.0160.0150.0220.0200.0030.0040.0000.0000.0110.0110.0100.0080.0030.0070.0000.0000.0030.0470.0430.0030.0450.008
Soil_Type170.0180.1930.1320.0380.0250.0300.0400.0920.0590.0440.0050.0190.0110.0180.0130.0020.0000.0051.0000.0040.0060.0090.0100.0020.0190.0260.0150.0010.0050.0030.0030.0380.0070.0180.0160.0240.0220.0040.0040.0000.0000.0130.0120.0110.0090.0040.0080.0000.0000.0030.0490.0690.0180.0520.053
Soil_Type180.0460.0590.1900.0500.0350.0220.1570.0200.0730.0480.0040.0140.0080.0130.0100.0000.0000.0040.0041.0000.0040.0060.0070.0010.0140.0190.0110.0000.0030.0020.0010.0280.0050.0130.0120.0180.0170.0020.0030.0000.0000.0090.0090.0080.0070.0020.0060.0000.0000.0020.0390.0590.0030.0500.015
Soil_Type190.0110.0480.0680.0580.0540.0490.0250.0560.0920.1060.0060.0200.0120.0190.0150.0020.0000.0060.0060.0041.0000.0090.0100.0030.0210.0280.0160.0020.0050.0030.0030.0410.0070.0190.0180.0260.0240.0040.0040.0000.0010.0140.0130.0120.0100.0040.0090.0000.0000.0030.0510.0390.0370.0450.022
Soil_Type20.0640.3100.2540.0190.0370.0420.0970.0470.0930.0210.0080.0280.0170.0270.0200.0030.0000.0080.0090.0060.0091.0000.0140.0040.0280.0380.0220.0030.0070.0050.0040.0570.0100.0270.0250.0360.0330.0060.0060.0000.0020.0190.0180.0170.0140.0060.0120.0000.0010.0050.0300.1030.0270.0640.104
Soil_Type200.0450.0450.0880.0680.0550.0360.1220.1020.0890.0790.0090.0310.0190.0300.0220.0040.0000.0090.0100.0070.0100.0141.0000.0040.0310.0420.0250.0030.0080.0050.0050.0630.0110.0300.0270.0400.0370.0060.0070.0000.0020.0210.0200.0190.0160.0060.0130.0000.0010.0050.0660.0720.0260.0440.033
Soil_Type210.0480.0470.0240.0460.0310.0320.0300.0480.0370.0260.0020.0090.0050.0090.0060.0000.0000.0020.0020.0010.0030.0040.0041.0000.0090.0120.0070.0000.0020.0000.0000.0190.0030.0090.0080.0120.0110.0010.0010.0000.0000.0060.0060.0050.0040.0010.0040.0000.0000.0000.0240.0340.0090.0430.010
Soil_Type220.0420.2120.2240.0460.0410.0390.0540.0570.1330.0570.0180.0600.0360.0580.0430.0080.0000.0170.0190.0140.0210.0280.0310.0091.0000.0820.0480.0070.0160.0110.0100.1230.0230.0580.0530.0780.0720.0130.0140.0030.0050.0410.0380.0360.0300.0130.0260.0030.0040.0110.0760.0690.1220.0920.064
Soil_Type230.0280.1870.1930.1410.1230.1310.0790.1670.0500.2120.0240.0810.0490.0770.0580.0110.0000.0230.0260.0190.0280.0380.0420.0120.0821.0000.0650.0090.0220.0140.0130.1650.0300.0780.0710.1050.0960.0170.0190.0040.0070.0550.0520.0490.0410.0170.0350.0040.0060.0150.1620.0300.1350.0480.087
Soil_Type240.1590.0840.0880.0280.1220.1410.0700.0380.0680.1140.0140.0480.0290.0450.0340.0060.0000.0140.0150.0110.0160.0220.0250.0070.0480.0651.0000.0050.0130.0080.0080.0970.0180.0460.0420.0610.0570.0100.0110.0020.0040.0320.0300.0290.0240.0100.0210.0020.0030.0080.0410.1220.0430.1290.051
Soil_Type250.0590.0150.0360.0360.0400.0100.0740.0270.0730.0360.0010.0070.0040.0060.0050.0000.0000.0010.0010.0000.0020.0030.0030.0000.0070.0090.0051.0000.0010.0000.0000.0140.0020.0060.0060.0090.0080.0000.0000.0000.0000.0040.0040.0040.0030.0000.0020.0000.0000.0000.1390.0260.1230.0250.007
Soil_Type260.0740.0530.0710.0330.0380.0330.0730.0190.0780.0450.0040.0160.0100.0150.0120.0010.0000.0040.0050.0030.0050.0070.0080.0020.0160.0220.0130.0011.0000.0020.0020.0330.0060.0160.0140.0210.0190.0030.0030.0000.0000.0110.0100.0100.0080.0030.0070.0000.0000.0020.0220.0600.0150.0760.017
Soil_Type270.0670.0200.0410.0550.0470.0240.0440.1110.0500.0510.0030.0100.0060.0100.0070.0000.0000.0020.0030.0020.0030.0050.0050.0000.0110.0140.0080.0000.0021.0000.0000.0210.0040.0100.0090.0140.0120.0010.0020.0000.0000.0070.0060.0060.0050.0010.0040.0000.0000.0010.1170.0390.0100.0490.011
Soil_Type280.0690.0370.0620.1730.1520.0080.0380.0330.0710.0900.0020.0100.0060.0090.0070.0000.0000.0020.0030.0010.0030.0040.0050.0000.0100.0130.0080.0000.0020.0001.0000.0200.0030.0090.0080.0130.0120.0010.0010.0000.0000.0060.0060.0060.0050.0010.0040.0000.0000.0000.1230.0360.0090.0460.010
Soil_Type290.1110.1970.2380.0920.0840.0800.2160.0720.3310.0950.0360.1210.0730.1160.0870.0160.0000.0350.0380.0280.0410.0570.0630.0190.1230.1650.0970.0140.0330.0210.0201.0000.0450.1160.1070.1570.1440.0260.0280.0070.0110.0830.0780.0730.0610.0260.0530.0060.0090.0220.0900.5510.1140.4370.130
Soil_Type30.1060.3260.2270.1260.0560.0360.1000.0430.1010.1440.0060.0220.0130.0210.0160.0020.0000.0060.0070.0050.0070.0100.0110.0030.0230.0300.0180.0020.0060.0040.0030.0451.0000.0210.0200.0290.0260.0040.0050.0000.0010.0150.0140.0130.0110.0040.0100.0000.0000.0040.0350.0830.0210.0100.167
Soil_Type300.1320.1480.1230.1320.1380.0430.0740.0540.1210.0810.0170.0570.0350.0550.0410.0070.0000.0160.0180.0130.0190.0270.0300.0090.0580.0780.0460.0060.0160.0100.0090.1160.0211.0000.0500.0740.0680.0120.0130.0030.0050.0390.0360.0350.0290.0120.0250.0030.0040.0100.0240.2590.0540.2060.061
Soil_Type310.0920.0800.0930.0570.0830.0510.0780.0750.1450.0840.0150.0520.0320.0500.0380.0070.0000.0150.0160.0120.0180.0250.0270.0080.0530.0710.0420.0060.0140.0090.0080.1070.0200.0501.0000.0680.0620.0110.0120.0020.0040.0360.0330.0320.0270.0110.0230.0020.0030.0090.0440.1940.0340.2370.056
Soil_Type320.0950.1070.1900.1260.0980.1210.1060.1390.1580.1400.0230.0770.0470.0730.0550.0100.0000.0220.0240.0180.0260.0360.0400.0120.0780.1050.0610.0090.0210.0140.0130.1570.0290.0740.0681.0000.0910.0170.0180.0040.0070.0520.0490.0470.0390.0160.0340.0040.0050.0140.0560.2840.0290.3130.082
Soil_Type330.0660.0940.0980.1270.1000.1290.0910.0990.1360.2310.0210.0710.0430.0680.0510.0090.0000.0200.0220.0170.0240.0330.0370.0110.0720.0960.0570.0080.0190.0120.0120.1440.0260.0680.0620.0911.0000.0150.0160.0040.0060.0480.0450.0430.0360.0150.0310.0030.0050.0130.1660.2620.0140.2940.076
Soil_Type340.0610.0430.0330.0270.0190.0380.0460.1050.0460.0130.0030.0130.0080.0120.0090.0000.0000.0030.0040.0020.0040.0060.0060.0010.0130.0170.0100.0000.0030.0010.0010.0260.0040.0120.0110.0170.0151.0000.0020.0000.0000.0090.0080.0080.0060.0020.0050.0000.0000.0010.0750.0480.0120.0600.014
Soil_Type350.0300.1490.1360.0300.0290.0150.0500.0360.0510.0240.0040.0140.0080.0130.0100.0000.0000.0040.0040.0030.0040.0060.0070.0010.0140.0190.0110.0000.0030.0020.0010.0280.0050.0130.0120.0180.0160.0021.0000.0000.0000.0090.0090.0080.0070.0020.0060.0000.0000.0020.0200.0120.0550.0050.015
Soil_Type360.0370.0380.0400.0130.0090.0160.0210.0670.0330.0150.0000.0030.0010.0030.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0040.0020.0000.0000.0000.0000.0070.0000.0030.0020.0040.0040.0000.0001.0000.0000.0020.0010.0010.0000.0000.0000.0000.0000.0000.0220.0130.0030.0160.003
Soil_Type370.0270.1180.0670.0240.0120.0240.0350.0170.0470.0090.0000.0050.0030.0050.0040.0000.0000.0000.0000.0000.0010.0020.0020.0000.0050.0070.0040.0000.0000.0000.0000.0110.0010.0050.0040.0070.0060.0000.0000.0001.0000.0030.0030.0030.0020.0000.0020.0000.0000.0000.0050.0150.0050.0100.006
Soil_Type380.0320.3410.3860.0570.0390.0460.0930.0700.0910.0740.0120.0400.0240.0390.0290.0050.0000.0110.0130.0090.0140.0190.0210.0060.0410.0550.0320.0040.0110.0070.0060.0830.0150.0390.0360.0520.0480.0090.0090.0020.0031.0000.0260.0240.0200.0090.0180.0010.0020.0070.0200.0110.0610.0170.043
Soil_Type390.0460.3310.3130.0870.0700.0720.0440.0430.1110.0960.0110.0380.0230.0360.0270.0050.0000.0110.0120.0090.0130.0180.0200.0060.0380.0520.0300.0040.0100.0060.0060.0780.0140.0360.0330.0490.0450.0080.0090.0010.0030.0261.0000.0230.0190.0080.0170.0010.0020.0070.0540.0130.0110.0020.041
Soil_Type40.1770.3420.2390.0550.0480.0780.0870.0500.0950.1380.0110.0360.0220.0340.0260.0040.0000.0100.0110.0080.0120.0170.0190.0050.0360.0490.0290.0040.0100.0060.0060.0730.0130.0350.0320.0470.0430.0080.0080.0010.0030.0240.0231.0000.0180.0080.0160.0010.0020.0060.0350.1330.0340.1380.022
Soil_Type400.0340.2660.6220.0400.0170.0410.0640.1660.0690.0310.0090.0300.0180.0290.0220.0040.0000.0080.0090.0070.0100.0140.0160.0040.0300.0410.0240.0030.0080.0050.0050.0610.0110.0290.0270.0390.0360.0060.0070.0000.0020.0200.0190.0181.0000.0060.0130.0000.0010.0050.2140.0120.1050.0430.032
Soil_Type50.0260.1630.2880.0590.0710.0790.0660.0160.0990.0850.0030.0130.0080.0120.0090.0000.0000.0030.0040.0020.0040.0060.0060.0010.0130.0170.0100.0000.0030.0010.0010.0260.0040.0120.0110.0160.0150.0020.0020.0000.0000.0090.0080.0080.0061.0000.0050.0000.0000.0010.0340.0470.0120.0460.201
Soil_Type60.0260.2820.3110.0160.0240.0170.1080.0410.1410.0120.0080.0260.0160.0250.0190.0030.0000.0070.0080.0060.0090.0120.0130.0040.0260.0350.0210.0020.0070.0040.0040.0530.0100.0250.0230.0340.0310.0050.0060.0000.0020.0180.0170.0160.0130.0051.0000.0000.0010.0040.0470.0970.0250.0940.410
Soil_Type70.0070.0130.0200.0110.0110.0060.0820.0190.0540.0200.0000.0030.0010.0030.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0040.0020.0000.0000.0000.0000.0060.0000.0030.0020.0040.0030.0000.0000.0000.0000.0010.0010.0010.0000.0000.0001.0000.0000.0000.0080.0150.0030.0120.003
Soil_Type80.0040.0100.0260.0160.0170.0140.0420.0100.0470.0350.0000.0040.0020.0040.0020.0000.0000.0000.0000.0000.0000.0010.0010.0000.0040.0060.0030.0000.0000.0000.0000.0090.0000.0040.0030.0050.0050.0000.0000.0000.0000.0020.0020.0020.0010.0000.0010.0001.0000.0000.0110.0190.0040.0150.004
Soil_Type90.0310.0330.1530.0270.0260.0130.0520.0250.0620.0340.0030.0110.0060.0100.0080.0000.0000.0030.0030.0020.0030.0050.0050.0000.0110.0150.0080.0000.0020.0010.0000.0220.0040.0100.0090.0140.0130.0010.0020.0000.0000.0070.0070.0060.0050.0010.0040.0000.0001.0000.0280.0490.0100.0390.011
Vertical_Distance_To_Hydrology0.0730.0960.0870.038-0.129-0.096-0.0430.619-0.0380.3010.0230.0670.0300.0710.0870.0220.0000.0470.0490.0390.0510.0300.0660.0240.0760.1620.0410.1390.0220.1170.1230.0900.0350.0240.0440.0560.1660.0750.0200.0220.0050.0200.0540.0350.2140.0340.0470.0080.0110.0281.0000.1850.0630.1450.094
Wilderness_Area10.2090.3110.2790.1870.2110.0990.3810.1090.4830.2310.0650.2200.1330.2580.1590.0290.0000.0430.0690.0590.0390.1030.0720.0340.0690.0300.1220.0260.0600.0390.0360.5510.0830.2590.1940.2840.2620.0480.0120.0130.0150.0110.0130.1330.0120.0470.0970.0150.0190.0490.1851.0000.2100.7940.235
Wilderness_Area20.0710.1500.2980.0550.0280.0490.1130.0680.2260.0410.0170.0570.0340.0540.0290.0070.0000.0030.0180.0030.0370.0270.0260.0090.1220.1350.0430.1230.0150.0100.0090.1140.0210.0540.0340.0290.0140.0120.0550.0030.0050.0610.0110.0340.1050.0120.0250.0030.0040.0100.0630.2101.0000.2050.061
Wilderness_Area30.1910.1120.1990.1150.1320.0670.3100.1360.3990.1480.0640.0070.1540.2050.1950.0020.0000.0450.0520.0500.0450.0640.0440.0430.0920.0480.1290.0250.0760.0490.0460.4370.0100.2060.2370.3130.2940.0600.0050.0160.0100.0170.0020.1380.0430.0460.0940.0120.0150.0390.1450.7940.2051.0000.229
Wilderness_Area40.1300.7360.8810.1860.2570.2200.3170.1030.3570.2760.2780.4850.0090.0610.0460.0700.0070.0080.0530.0150.0220.1040.0330.0100.0640.0870.0510.0070.0170.0110.0100.1300.1670.0610.0560.0820.0760.0140.0150.0030.0060.0430.0410.0220.0320.2010.4100.0030.0040.0110.0940.2350.0610.2291.000

Missing values

2025-12-11T14:19:26.447024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-11T14:19:29.049459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area1Wilderness_Area2Wilderness_Area3Wilderness_Area4Soil_Type1Soil_Type2Soil_Type3Soil_Type4Soil_Type5Soil_Type6Soil_Type7Soil_Type8Soil_Type9Soil_Type10Soil_Type11Soil_Type12Soil_Type13Soil_Type14Soil_Type15Soil_Type16Soil_Type17Soil_Type18Soil_Type19Soil_Type20Soil_Type21Soil_Type22Soil_Type23Soil_Type24Soil_Type25Soil_Type26Soil_Type27Soil_Type28Soil_Type29Soil_Type30Soil_Type31Soil_Type32Soil_Type33Soil_Type34Soil_Type35Soil_Type36Soil_Type37Soil_Type38Soil_Type39Soil_Type40Cover_Type
0259651325805102212321486279100000000000000000000000000000001000000000005
12590562212-63902202351516225100000000000000000000000000000001000000000005
2280413992686531802342381356121100000000000000100000000000000000000000000002
327851551824211830902382381226211100000000000000000000000000000000100000000002
42595452153-13912202341506172100000000000000000000000000000001000000000005
525791326300-15672302371406031100000000000000000000000000000001000000000002
6260645727056332222251386256100000000000000000000000000000001000000000005
7260549423475732222301446228100000000000000000000000000000001000000000005
82617459240566662232211336244100000000000000000000000000000001000000000005
926125910247116362282191246230100000000000000000000000000000001000000000005
ElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area1Wilderness_Area2Wilderness_Area3Wilderness_Area4Soil_Type1Soil_Type2Soil_Type3Soil_Type4Soil_Type5Soil_Type6Soil_Type7Soil_Type8Soil_Type9Soil_Type10Soil_Type11Soil_Type12Soil_Type13Soil_Type14Soil_Type15Soil_Type16Soil_Type17Soil_Type18Soil_Type19Soil_Type20Soil_Type21Soil_Type22Soil_Type23Soil_Type24Soil_Type25Soil_Type26Soil_Type27Soil_Type28Soil_Type29Soil_Type30Soil_Type31Soil_Type32Soil_Type33Soil_Type34Soil_Type35Soil_Type36Soil_Type37Soil_Type38Soil_Type39Soil_Type40Cover_Type
58100224191682510833124230240126812001001000000000000000000000000000000000000003
5810032415161259529120236237116815001001000000000000000000000000000000000000003
5810042410158249024120238236115819001001000000000000000000000000000000000000003
5810052405159229019120237238119824001001000000000000000000000000000000000000003
5810062401157219015120238238119830001001000000000000000000000000000000000000003
5810072396153208517108240237118837001001000000000000000000000000000000000000003
581008239115219671295240237119845001001000000000000000000000000000000000000003
58100923861591760790236241130854001001000000000000000000000000000000000000003
58101023841701560590230245143864001001000000000000000000000000000000000000003
58101123831651360467231244141875001001000000000000000000000000000000000000003